This section provides an overview of the soil moisture datasets currently available, including their spatial resolution, latency, spatial coverage, and intended applications.
All products provide daily global coverage and are gap-filled in both time and space using data fusion and modeling techniques. This ensures spatially continuous datasets without missing regions, even in areas with sparse satellite observations.
Product foundations and measurement approach¶
Spire’s soil moisture products are derived primarily from GNSS-R satellite observations, combined with additional Earth observation datasets and modeling methods. GNSS-R measures reflected navigation signals from the Earth’s surface; the characteristics of these reflections are sensitive to surface moisture, roughness, and vegetation conditions.
Machine learning and multi-sensor data fusion are used to enhance spatial resolution, improve consistency across observation geometries, and fill observational gaps. This approach enables reliable global coverage, including regions where ground measurements are limited or unavailable.
Long historical archives support analysis of variability and trends over multi-year to multi-decadal timescales, while daily updates and API access enable integration into automated workflows, monitoring systems, and decision-support tools.
Related overview: Introducing Spire’s Soil Moisture Insights
Soil moisture observations¶
Primary datasets providing volumetric soil moisture estimates at different spatial scales:
D-MSSM (6 km) — Daily Medium-resolution Gap-filled Surface Soil Moisture
D-ESSM (500 m) — Daily Enhanced Surface Soil Moisture
D-HSSM (100 m) — Daily High-resolution Surface Soil Moisture
Soil moisture forecast¶
Predictive estimates of future soil moisture conditions based on current observations and model inputs:
Soil moisture anomalies¶
Indicators of unusually wet or dry conditions relative to historical climatology:
Technical reference¶
Reference information applicable across all products: